Staining-free cell viability measurement technique using lens-free shadow imaging platform

Roy, Mohendra; Jin, Geonsoo; Pan, Jeong Hoon; Seo, Dongmin; Hwang, Yongha; Oh, Sangwoo; Lee, Moonjin; Kim, Young Jun; Seo, Sungkyu

doi:10.1016/j.snb.2015.10.097

Cited by 20 publications

(11 citation statements)

References 19 publications

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“…Similarly, a mean error of 3.4% and 13.7% was obtained for lymphocytes and monocytes, respectively ( Figure 5 b,c). Using the shadow parameters developed in this study, the average error rate for the classification of neutrophils, lymphocytes, and monocytes was better than that of our previously reported method ( Figure 5 g) [ 32 ]. Furthermore, the average correlation was 0.999, which was also much better than in the previous study.…”

Section: Resultsmentioning

confidence: 50%

“…We have previously reported that heterogeneous solutions of erythrocytes, yeast cells, E. coli , and microparticles of different sizes can be characterized automatically without lenses or microscope objectives. LSIT can be used to determine hemoglobin concentration [ 29 , 30 ], analyze the growth of bacterial biofilms [ 31 ], and perform various cell counts [ 27 , 32 ]. LSIT has advantages such as compactness, low cost, and high throughput because it does not require structures for optical lenses and alignments [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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Field-Portable Leukocyte Classification Device Based on Lens-Free Shadow Imaging Technique

et al. 2022

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The complete blood count (CBC) is one of the most important clinical steps in clinical diagnosis. The instruments used for CBC are usually expensive and bulky and require well-trained operators. Therefore, it is difficult for medical institutions below the tertiary level to provide these instruments, especially in underprivileged countries. Several reported on-chip blood cell tests are still in their infancy and do not deviate from conventional microscopic or impedance measurement methods. In this study, we (i) combined magnetically activated cell sorting and the differential density method to develop a method to selectively isolate three types of leukocytes from blood and obtain samples with high purity and concentration for portable leukocyte classification using the lens-free shadow imaging technique (LSIT), and (ii) established several shadow parameters to identify the type of leukocytes in a complete leukocyte shadow image by shadow image analysis. The purity of the separated leukocytes was confirmed by flow cytometry. Several shadow parameters such as the “order ratio” and “minimum ratio” were developed to classify the three types of leukocytes. A shadow image library corresponding to each type of leukocyte was created from the tested samples. Compared with clinical reference data, a correlation index of 0.98 was obtained with an average error of 6% and a confidence level of 95%. This technique offers great potential for biological, pharmaceutical, environmental, and clinical applications, especially where point-of-care detection of rare cells is required.

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Section: Resultsmentioning

confidence: 50%

Section: Introductionmentioning

confidence: 99%

Field-Portable Leukocyte Classification Device Based on Lens-Free Shadow Imaging Technique

et al. 2022

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“…When the projectile passes through the image surface, part of the parallel light is blocked, and a dark area forms on the image sensor. The boundary between the bright and dark areas is the projection of the projectile [24][25][26][27][28][29]. The position of the projection of the light screen and the position of the projectile on the image sensor are separately recorded by the high-speed camera [30][31][32], and the target deviation of the projectile can be obtained according to the pixel value of the image sensor.…”

Section: Principlementioning

confidence: 99%

Research on Target Deviation Measurement of Projectile Based on Shadow Imaging Method in Laser Screen Velocity Measuring System

Chu

Zhao

Liu

et al. 2020

Sensors

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In the laser screen velocity measuring (LSVM) system, there is a deviation in the consistency of the optoelectronic response between the start light screen and the stop light screen. When the projectile passes through the light screen, the projectile’s over-target position, at which the timing pulse of the LSVM system is triggered, deviates from the actual position of the light screen (i.e., the target deviation). Therefore, it brings errors to the measurement of the projectile’s velocity, which has become a bottleneck, affecting the construction of a higher precision optoelectronic velocity measuring system. To solve this problem, this paper proposes a method based on high-speed shadow imaging to measure the projectile’s target deviation, ΔS, when the LSVM system triggers the timing pulse. The infrared pulse laser is collimated by the combination of the aspherical lens to form a parallel laser source that is used as the light source of the system. When the projectile passes through the light screen, the projectile’s over-target signal is processed by the specially designed trigger circuit. It uses the rising and falling edges of this signal to trigger the camera and pulsed laser source, respectively, to ensure that the projectile’s over-target image is adequately exposed. By capturing the images of the light screen of the LSVM system and the over-target projectile separately, this method of image edge detection was used to calculate the target deviation, and this value was used to correct the target distance of the LSVM to improve the accuracy of the measurement of the projectile’s velocity.

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“…Since this arrangement consists of a few components, most of which are easily available and at a low price, therefore it reduces the overall cost of the system [2]. This simple and cost-effective nature facilitates the feasibility of the LSIT for applications in the field of point-of-care systems or telemedicine systems [3,4,5]. Recent advancements in machine learning, especially deep learning, have facilitated many applications concerning medical diagnostics [6,7,8,9,10,11,12], and have been widely adopted in the field of microscopy [13,14,15].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Lens-Free Shadow Imaging Technique for Field-Portable Cytometry

et al. 2022

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The lens-free shadow imaging technique (LSIT) is a well-established technique for the characterization of microparticles and biological cells. Due to its simplicity and cost-effectiveness, various low-cost solutions have been developed, such as automatic analysis of complete blood count (CBC), cell viability, 2D cell morphology, 3D cell tomography, etc. The developed auto characterization algorithm so far for this custom-developed LSIT cytometer was based on the handcrafted features of the cell diffraction patterns from the LSIT cytometer, that were determined from our empirical findings on thousands of samples of individual cell types, which limit the system in terms of induction of a new cell type for auto classification or characterization. Further, its performance suffers from poor image (cell diffraction pattern) signatures due to their small signal or background noise. In this work, we address these issues by leveraging the artificial intelligence-powered auto signal enhancing scheme such as denoising autoencoder and adaptive cell characterization technique based on the transfer of learning in deep neural networks. The performance of our proposed method shows an increase in accuracy >98% along with the signal enhancement of >5 dB for most of the cell types, such as red blood cell (RBC) and white blood cell (WBC). Furthermore, the model is adaptive to learn new type of samples within a few learning iterations and able to successfully classify the newly introduced sample along with the existing other sample types.

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Staining-free cell viability measurement technique using lens-free shadow imaging platform

Cited by 20 publications

References 19 publications

Field-Portable Leukocyte Classification Device Based on Lens-Free Shadow Imaging Technique

Field-Portable Leukocyte Classification Device Based on Lens-Free Shadow Imaging Technique

Research on Target Deviation Measurement of Projectile Based on Shadow Imaging Method in Laser Screen Velocity Measuring System

Machine Learning Based Lens-Free Shadow Imaging Technique for Field-Portable Cytometry

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